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# Copyright 2021 Huawei Technologies Co., Ltd
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import torch
import numpy as np
import torch.npu
import os
NPU_CALCULATE_DEVICE = 0
if os.getenv('NPU_CALCULATE_DEVICE') and str.isdigit(os.getenv('NPU_CALCULATE_DEVICE')):
    NPU_CALCULATE_DEVICE = int(os.getenv('NPU_CALCULATE_DEVICE'))
if torch.npu.current_device() != NPU_CALCULATE_DEVICE:
    torch.npu.set_device(f'npu:{NPU_CALCULATE_DEVICE}')


def calc_patch_size(func):
    def wrapper(args):
        if args.scale == 2:
            args.patch_size = 10
        elif args.scale == 3:
            args.patch_size = 7
        elif args.scale == 4:
            args.patch_size = 6
        else:
            raise Exception('Scale Error', args.scale)
        return func(args)
    return wrapper


def convert_rgb_to_y(img, dim_order='hwc'):
    if dim_order == 'hwc':
        return 16. + (64.738 * img[..., 0] + 129.057 * img[..., 1] + 25.064 * img[..., 2]) / 256.
    else:
        return 16. + (64.738 * img[0] + 129.057 * img[1] + 25.064 * img[2]) / 256.


def convert_rgb_to_ycbcr(img, dim_order='hwc'):
    if dim_order == 'hwc':
        y = 16. + (64.738 * img[..., 0] + 129.057 * img[..., 1] + 25.064 * img[..., 2]) / 256.
        cb = 128. + (-37.945 * img[..., 0] - 74.494 * img[..., 1] + 112.439 * img[..., 2]) / 256.
        cr = 128. + (112.439 * img[..., 0] - 94.154 * img[..., 1] - 18.285 * img[..., 2]) / 256.
    else:
        y = 16. + (64.738 * img[0] + 129.057 * img[1] + 25.064 * img[2]) / 256.
        cb = 128. + (-37.945 * img[0] - 74.494 * img[1] + 112.439 * img[2]) / 256.
        cr = 128. + (112.439 * img[0] - 94.154 * img[1] - 18.285 * img[2]) / 256.
    return np.array([y, cb, cr]).transpose([1, 2, 0])


def convert_ycbcr_to_rgb(img, dim_order='hwc'):
    if dim_order == 'hwc':
        r = 298.082 * img[..., 0] / 256. + 408.583 * img[..., 2] / 256. - 222.921
        g = 298.082 * img[..., 0] / 256. - 100.291 * img[..., 1] / 256. - 208.120 * img[..., 2] / 256. + 135.576
        b = 298.082 * img[..., 0] / 256. + 516.412 * img[..., 1] / 256. - 276.836
    else:
        r = 298.082 * img[0] / 256. + 408.583 * img[2] / 256. - 222.921
        g = 298.082 * img[0] / 256. - 100.291 * img[1] / 256. - 208.120 * img[2] / 256. + 135.576
        b = 298.082 * img[0] / 256. + 516.412 * img[1] / 256. - 276.836
    return np.array([r, g, b]).transpose([1, 2, 0])


def preprocess(img, device):
    img = np.array(img).astype(np.float32)
    ycbcr = convert_rgb_to_ycbcr(img)
    x = ycbcr[..., 0]
    x /= 255.
    x = torch.from_numpy(x).to(f'npu:{NPU_CALCULATE_DEVICE}')
    x = x.unsqueeze(0).unsqueeze(0)
    return x, ycbcr


def calc_psnr(img1, img2):
    return 10. * torch.log10(1. / torch.mean((img1 - img2) ** 2))


class AverageMeter(object):
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count
